README.md

metadata

license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
  - text-to-cad
  - code-generation
  - cadquery
  - 3d-modeling
  - reinforcement-learning
language:
  - en
pipeline_tag: text-generation
library_name: transformers

CAD-Coder

CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward

Accepted at NeurIPS 2025 (Poster)

This is the reinforcement learning (GRPO) fine-tuned model for generating CadQuery code from natural language descriptions.

Model Description

CAD-Coder reformulates text-to-CAD as the generation of CadQuery scripts—a Python-based, parametric CAD language. The model is trained with a two-stage pipeline:

1. Supervised Fine-Tuning (SFT): Learning CadQuery syntax and text-to-code mapping
2. Reinforcement Learning (GRPO): Optimizing geometric accuracy with CAD-specific rewards (Chamfer Distance + Format Reward)

Key Features

• Generates executable CadQuery Python code from natural language
• Chain-of-Thought (CoT) reasoning for complex CAD structures
• Geometric reward optimization for accurate 3D model generation
• Supports diverse CAD operations beyond simple sketch-extrusion

Usage

For complete inference scripts, please visit our GitHub repository.

Installation

pip install transformers
pip install "numpy<2.0" cadquery==2.3.1  # Optional: for code execution

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "gudo7208/CAD-Coder"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

prompt = "Create a cylinder with radius 10mm and height 20mm, with a central hole of radius 5mm."

text = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    tokenize=False,
    add_generation_prompt=True
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Performance

Method	Mean CD	Median CD	IR%
Text2CAD	29.29	0.37	3.75
CAD-Coder (Ours)	6.54	0.17	1.45

CD metrics are ×10³. Lower is better.

Training Details

• Base Model: Qwen2.5-7B-Instruct
• Training Data: 110K text-CadQuery-3D model triplets + 1.5K CoT samples
• Hardware: 8× NVIDIA A800 80GB GPUs
• Framework: Hugging Face Transformers, DeepSpeed, Verl (GRPO)

Citation

@article{guan2025cadcoder,
  title={CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward},
  author={Guan, Yandong and Wang, Xilin and Xing, Ximing and Zhang, Jing and Xu, Dong and Yu, Qian},
  journal={arXiv preprint arXiv:2505.19713},
  year={2025}
}

License

This model is released under the Apache 2.0 License, following the base model (Qwen2.5-7B-Instruct) license terms.

Acknowledgements

• Base model: Qwen2.5-7B-Instruct
• Training data derived from Text2CAD dataset